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When working with real-world data as a Data Analyst, encountering missing or null values is quite prevalent. This phenomenon is referred to as "Missing Data" in the field of data analysis. Missing data can severely impact the results of a data analysis process since it reduces the statistical power, which can distort the reliability and robustness of outcomes.
Missing data is a part of the 'Data Cleaning' step which is a crucial part of the Preprocessing in Data Analytics. It involves identifying incomplete, incorrect or irrelevant data and then replacing, modifying or deleting this dirty data. Successful data cleaning of missing values can significantly augment the overall quality of the data, therefore offering valuable and reliable insights. It is essential for a Data Analyst to understand the different techniques for dealing with missing data, such as different types of imputations based on the nature of the data and research question.
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